Facial detection and facial recognition algorithms exist and are available on the market. Currently, these algorithms (especially facial recognition algorithms) still face accuracy challenges when applied in certain settings and configurations. For example, facial detection and recognition algorithms tend to be highly inaccurate at identifying and recognizing facial profiles, or even facial images that are directed more than 20° away from the image source. Additionally, facial detection and recognition algorithms tend to have difficulty accurately detecting and recognizing faces in poor lighting or when objects partially cover the subject's face, such as for subjects wearing sunglasses and/or having long hair. Facial recognition algorithms also tend to miss or misidentify certain facial features in an image, thus making it difficult to accurately compare and match two different facial images. For example, recognition is sensitive to temporal changes that an individual may have, such as changing a hair style or growing a beard. Recognition is also sensitive to the amount of possible matches in a database, which may increase the possibility of a mismatch.